Sramana: What do you need to build these models in terms of data sets? How do you derive them?

Hemant Shah: The thing that we really stayed true to was recognizing what our core competency was and was not. When we made the shift from earthquake risk to natural hazard risk, we knew that we did not need to be the world’s smartest people on catastrophic peril. We needed to get really good at bridging between the multidisciplinary knowledge that is out there in the world. Our value ad was combining that multidisciplinary knowledge and translating it into a business risk model.

What enabled us to scale from the product standpoint was realizing where our core competency was. Certainly we had to have a critical mass of expertise, and today we have hundreds of scientist in-house. At the time, we chose to stay very true to the differentiated technology and know-how that would allow us to add value. That enabled us to harness the knowledge of a lot of scientific disciplines and combine that into a decision support tool and a risk model with a specific focus on the decisions that insurance and reinsurance companies had to make. In many ways our innovations were not just around the science of the catastrophes, but how you translate that into financial risk metrics that support insurance and reinsurance decisions.

It was very helpful for us to be clear about who our customer was. That differentiated value add is not generically how we make earthquake or catastrophic information more available. It is about how we make that information available to a specific industry making specific decisions. In the early days, the work that I did was with the customer around a whiteboard. I had them explain to me how they ran their business, how they made a decision, what data they collected, and how they priced a contract. They described the vernacular and semantics of their business.

Hemant Shah: It was all about making a better decision. We wanted them to understand their existing model, and then we could backward chain that into the problems that we uniquely understood how to solve. It was always about building software and models that we could license.

Sramana: How many customers did you build over the course of this journey who were really your lighthouse customers?

Hemant Shah: Our business today has revenues of around 300 million dollars. We have a few hundred institutional clients. We are very focused on relatively large financial services companies. That gives us the inherent luxury of focus. I can count the CEOs, and I know how they think.

Sramana: Did you get inputs on product direction from all of these customers?

Hemant Shah: Today we are fortunate because of the work we have done over the past 20 years. We have institutional relationships, and they give us data. We have feedback loops because our customers share their loss data with us, which helps us train our models. To create those cycles is difficult. Until you get that information you can’t combine it with other data.

In the early days we institutionalized these development partnerships. Every time we set out to address a new peril, we would form development consortia with our clients. We would get five or six of our clients around a table and we would have them all teach us about the data they collected and how they made decisions. It was a very collaborative product design and development. That kept us focused on real world decisions instead of getting lost in deep science.